1. Field of the Invention
The present invention relates to an image processing apparatus and method, more particularly to an image processing apparatus and method for determining whether noise is present in image data.
2. Description of the Related Art
The quality requirements for video content are continuously being raised as a result of the rapid advances in signal processing technology. Video content with high picture quality and high resolution is already being widely used in various types of image display apparatuses, such as high-definition televisions (HDTVs), LCD monitors for personal computers, etc.
Video content typically includes a plurality of frames or fields. During processing or transmission of video signals, it is common for some of the frames or fields to be affected by noise to thereby undergo change (e.g., some pixel values change). High-resolution display apparatuses are more sensitive to noise in video content. That is, when video content is subjected to noise interference, the effect is more pronounced in high-resolution display apparatuses, such that the quality of the video content is reduced. Therefore, the determination of the level of noise and ways in which noise may be suppressed or eliminated are critical issues in the area of video processing technology.
Mean absolute difference (MAD) is a commonly used technique for determining whether video content is being subjected to noise. The formula for MAD is as shown in the following Formula (F1):
                              M          ⁢                                          ⁢          A          ⁢                                          ⁢                      D            ⁡                          (                              dx                ,                dy                            )                                      =                              1                          m              ·              n                                ⁢                                    ∑                              i                =                0                            n                        ⁢                                          ∑                                  j                  =                  0                                m                            ⁢                                                                                                          P                      k                                        ⁡                                          (                                              i                        ,                        j                                            )                                                        -                                                            P                                              k                        -                        1                                                              ⁡                                          (                                              i                        ,                        j                                            )                                                                                                                                              (                  F          ⁢          .1                )            
where m and n are resolution dimensions of video content, Pk(i,j) is a pixel value of a pixel at position (i,j) of a kth frame, and Pk−1(i,j) is a pixel value of a pixel at position (i,j) of a (k−1)th frame. “Pixel value” refers to the luminance value (luma) or the chromatic value (chroma) of a pixel.
An example is provided with reference to FIG. 1. In a consecutive frame sequence (S) including frame S1, S2 . . . Sk−1, Sk, Sk+1, etc., if an object Ok in the frame Sk is not moved relative to an object Ok−1 in the frame Sk−1, then in theory each pixel value in the frame Sk will be the same as the pixel value in a corresponding location in the frame Sk−1, and therefore, a MAD value of zero is calculated using Formula (F1).
If noise is present in the frame Sk, then at least one pixel value in the frame Sk is changed. Referring to FIG. 2, ignoring any affect due to dynamic imaging (i.e., movement in an image) and assuming that a pixel value (s) in the frame Sk is changed as a result of signal nose, then MADnoise may be calculated based on Formula (F1) as follows:
      M    ⁢                  ⁢    A    ⁢                  ⁢          D      noise        =            1      5.5        [                                                  45            -            40                                    +                                        35            -            35                                    +                                        32            -            32                                    +                                        41            -            41                                    +                                        40            -            40                                    +                                        47            -            20                                    +                                        30            -            26                                    +                                        20            -            25                                    +                                        36            -            30                                    +                                        35            -            35                                    +                                        40            -            20                                    +                                        20            -            20                                    +                                        18            -            20                                    +                                        24            -            24                                    +                                        11            -            27                                    +                                        20            -            20                                    +                                        18            -            20                                    +                                        20            -            20                                    +                                        10            -            18                                    +                                                                          26                -                26                                                    ++                    ⁢                                                20              -              20                                                  +                                         ❘                                          29                -                20                            ❘                              +                                  ❘                                                            20                      -                      20                                        ❘                                          +                                              ❘                                                                              10                            -                            20                                                    ❘                                                      +                                                          ❘                                                                                                36                                  -                                  25                                                                ❘                                                                                                                                                                                                                                      ]                    =      5      
The larger the value of MAD, the greater the amount of noise, that is, the greater the influence of noise on the pixels. Conversely, the lower the value of MAD, the smaller the amount of noise, that is, the smaller the influence of noise on the pixels. Hence, in the prior art, the presence of noise is determined according to how high or low the value of MAD is.
Referring to FIGS. 3 and 4, if it is assumed that an object Ok in the frame Sk is moved relative to an object Ok−1 in the frame Sk−1, then MADmotion is calculated as follows:
      M    ⁢                  ⁢    A    ⁢                  ⁢          D      motion        =                    1        5.5            ⁡              [                                                        20              -              40                                            +                                                20              -              35                                            +                                                26              -              32                                            +                                                25              -              41                                            +                                                30              -              40                                            +                                                20              -              20                                            +                                                20              -              26                                            +                                                20              -              25                                            +                                                20              -              30                                            +                                                24              -              35                                            +                                                20              -              20                                            +                                                20              -              20                                            +                                                20              -              20                                            +                                                20              -              24                                            +                                                18              -              27                                            +                                                20              -              20                                            +                                                20              -              20                                            +                                                20              -              20                                            +                                                20              -              18                                            +                                                                                      20                  -                  26                                                            ++                        ⁢                                                        20                -                20                                                            +                                                20              -              20                                            +                                                20              -              20                                            +                                                20              -              20                                            +                                                20              -              25                                                  ]              =    5  
From the foregoing, it is evident that with respect to the frame Sk, there is no difference between MADnoise occurring when there is noise and MADmotion occurring when there is object movement in a frame. Hence, using the conventional determination method, it is not possible to determine whether differences in pixel values between a current image and a previous image are due to noise interference or dynamic imaging.
When movement in an image is mistaken for noise interference, loss of image fidelity will occur. Since MAD is the result of object movement during dynamic imaging, each pixel value in the frame Sk is not necessarily related to the previous frame Sk−1. For example, due to object movement, pixel (i,j) may be part of an object in the frame Sk−1, while the same pixel (i,j) may be part of the background in the frame Sk. If this is mistaken for noise interference, and mean processing is performed for the pixel values of the previous and subsequent images in order to cancel the noise, the end result may be image streaking.